In the following all deconvolution tools for spaceDeconv are tested and run once to showcase and test all tools.

Imports and Python Setup

library(reticulate)
library(TENxVisiumData) # data
library(TabulaMurisSenisData) # data
library(ggspavis) # visualization
library(SPOTlight)
# setup python
reticulate::use_condaenv("cell2loc_env")
scanpy <- reticulate::import("scanpy")
reticulate::py_available()
## [1] TRUE

Datasets

We are using two sample datasets from mouse kidneys.

# Spatial
sp <- TENxVisiumData::MouseKidneyCoronal()
rownames(sp) <- rowData(sp)$symbol # overwrite EnsemblID

# Single Cell 
sc <- TabulaMurisSenisData::TabulaMurisSenisDroplet(tissues= "Kidney")$Kidney

Let’s have a look inside the datasets. The console output shows the available cell types and the corresponding number of available cells. The Plot visualizes the number of gene counts per spot and the corresponding Visium Image.

1m 3m 18m 21m 24m 30m
CD45 11 32 76 54 1010 106
CD45 B cell 7 5 45 54 2322 62
CD45 NK cell 1 4 8 2 47 4
CD45 T cell 8 18 48 42 846 177
CD45 macrophage 59 132 254 101 259 514
CD45 plasma cell 0 0 9 12 169 42
Epcam kidney distal convoluted tubule epithelial cell 78 126 169 153 0 131
Epcam brush cell 52 15 78 169 0 31
Epcam kidney collecting duct principal cell 77 110 132 58 0 370
Epcam kidney proximal convoluted tubule epithelial cell 945 684 1120 868 0 817
Epcam podocyte 92 94 64 66 0 170
Epcam proximal tube epithelial cell 25 195 296 5 0 1977
Epcam thick ascending tube S epithelial cell 465 312 248 228 0 313
Pecam Kidney cortex artery cell 75 78 91 69 0 115
Pecam fenestrated capillary endothelial 164 182 164 134 0 211
Pecam kidney capillary endothelial cell 49 38 25 18 0 7
Stroma fibroblast 15 16 37 13 0 80
Stroma kidney mesangial cell 80 51 18 22 0 7
nan 285 238 256 189 1068 579

Preprocessing

We subset the cells to remove unsuitable cells and NAs. We further subset cells for performance reasons. In a full run this step will be skipped.

This section will further include normalization steps.

# We are only using data from 18m old cells
sc <- sc[, sc$age=="18m"]
sc <- sc[, !sc$free_annotation %in% c("nan", "CD45")] # remove false data

# downsampling for performance reasons, copied from MarcElosua/SPOTlight
idx <- split(seq(ncol(sc)), sc$free_annotation)
n_cells <- 50
cs_keep <- lapply(idx, function(i) {
    n <- length(i)
    if (n < n_cells)
        n_cells <- n
    sample(i, n_cells)
})
sc <- sc[, unlist(cs_keep)]

SPOTlight

Marker Genes are necessary for SPOTlight. Here i follow the guideline presented on their website. This could be done differently. A vector of highly variable Genes is also helpful but not required.

# SPOTligth parameters
slot <- "cell_ontology_class" # where are the cell types stored, find better term for "slot"
mgs <- NULL # DataFrame of scored Marker genes
# + obviously the single cell and spatial dataset
colLabels(sc) <- colData(sc)[[slot]] # add Labels

# norm 
sc <- scuttle::logNormCounts(sc) # score marksers

mgs <- scran::scoreMarkers(sc) # can include a subset step, requires logcounts slot/assay?

# this code is from the vignette
# one could perform this differently but the final dataframe needs to be present
mgs_fil <- lapply(names(mgs), function(i) {
  x <- mgs[[i]]
  # Filter and keep relevant marker genes, those with AUC > 0.8
  x <- x[x$mean.AUC > 0.8, ]
  # Sort the genes from highest to lowest weight
  x <- x[order(x$mean.AUC, decreasing = TRUE), ]
  # Add gene and cluster id to the dataframe
  x$gene <- rownames(x)
  x$cluster <- i
  data.frame(x)
})
mgs_df <- do.call(rbind, mgs_fil)

deconvolution <- SPOTlight::SPOTlight(sc, sp, mgs=mgs_df, weight_id = "mean.AUC", verbose=TRUE)
## Warning in .set_groups_if_null(x): Grouping cells into celltypes
##                     by SingleCellExperiment::colLabels(x)
## Scaling count matrix
## Seeding initial matrices
## Training NMF model
## Time for training: 9.89min
## Deconvoluting mixture data
deconvolution <- deconvolution$mat # extract the result
knitr::kable(head(deconvolution[, 4:6])) # just a subset
fenestrated cell fibroblast kidney capillary endothelial cell
AAACCGTTCGTCCAGG-1 0.0424944 0.0000000 0.0223056
AAACCTAAGCAGCCGG-1 0.0000000 0.0000000 0.0000000
AAACGAGACGGTTGAT-1 0.0599849 0.0000000 0.0322275
AAACGGTTGCGAACTG-1 0.0000000 0.0000000 0.0000000
AAACTCGGTTCGCAAT-1 0.0985886 0.0329418 0.1109310
AAACTGCTGGCTCCAA-1 0.0282406 0.0000000 0.0123047 
# adding the results to colData
colnames(deconvolution) <- make.names(colnames(deconvolution))

for (i in colnames(deconvolution)){ # each separately
  sp[[i]] <- deconvolution[, i] 
}

# the colData annotation of the spatialExperiment now looks like this:
names(colData(sp))
##  [1] "sample_id"                                                
##  [2] "nCounts"                                                  
##  [3] "B.cell"                                                   
##  [4] "brush.cell"                                               
##  [5] "epithelial.cell.of.proximal.tubule"                       
##  [6] "fenestrated.cell"                                         
##  [7] "fibroblast"                                               
##  [8] "kidney.capillary.endothelial.cell"                        
##  [9] "kidney.collecting.duct.principal.cell"                    
## [10] "kidney.cortex.artery.cell"                                
## [11] "kidney.distal.convoluted.tubule.epithelial.cell"          
## [12] "kidney.loop.of.Henle.thick.ascending.limb.epithelial.cell"
## [13] "kidney.mesangial.cell"                                    
## [14] "kidney.proximal.convoluted.tubule.epithelial.cell"        
## [15] "lymphocyte"                                               
## [16] "macrophage"                                               
## [17] "NK.cell"                                                  
## [18] "plasma.cell"                                              
## [19] "podocyte"                                                 
## [20] "T.cell"

Cell2location